A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series

Mieslinger, Theresa; Ament, Felix; Chhatbar, Kaushal; Meyer, Richard

doi:10.1016/j.egypro.2014.02.182

Cited by 25 publications

(15 citation statements)

References 2 publications

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“…Another commonly used approach is regression [61][62][63]. However, the regression method suffers from the regression dilution effect, which means that the regression slope tends to be biased toward zero, which causes an underestimated scaling factor [64].…”

Section: Comparison With Other Fusion Methodsmentioning

confidence: 99%

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Feng

Wang

2018

Remote Sensing

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Surface incident solar radiation (R s ) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, R s data has been shown to have substantial spatial bias, and the time span of reliable satellite R s is too short for climatic and ecological studies. Combining reanalysis and satellite data would be an effective method for generating long-term and consistent R s datasets. Here, we apply a cumulative probability density function-based (CPDF) method to merge eight reanalyses with the latest available satellite R s data from Clouds and Earth's Radiant Energy System Energy Balanced and Filled (CERES EBAF) surface retrievals. The CPDF method not only reduces the spatial bias of the reanalysis R s data, but also makes the R s datasets in a global, long-term and consistent way. The observed R s data collected at 54 Baseline Surface Radiation Network (BSRN) stations from 1992 to 2016 are used to evaluate the method. Results show that the CPDF method could reduce the mean absolute biases (MAB) of the reanalysis R s effectively by 21.24-64.36%. The European Centre for Medium-Range Weather Forecasts Re-Analysis interim (ERA-interim) reanalysis R s data, which are available for 1979 onward, perform the best before MAB = 13.20 W·m −2 and after MAB = 10.40 W·m −2 merging. This small post-merging MAB of the ERA-interim reanalysis is caused by the MAB of 9.90 W·m −2 in the satellite R s retrievals. The Japanese 55-year reanalysis provides R s values back to 1958, and CPDF can reduce its MAB by 32.87%, to 11.17 W·m −2 . The National Oceanic and Atmospheric Administration (NOAA)-CIRES twentieth-century reanalysis (CIRES) and the ECMWF twentieth-century reanalysis (ERA20CM) provide century-long R s estimates. CIRES performs better after merging. The MAB of CIRES can be reduced by 32.10%, to 12.99 W·m −2 , while ERA20CM's can be reduced by 12.51%, to 16.40 W·m −2 .

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Section: Comparison With Other Fusion Methodsmentioning

confidence: 99%

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Feng

Wang

2018

Remote Sensing

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“…The method was tested against two high-quality ground stations: Plataforma Solar de Almería (southern Spain) and Tamanrasset (Algeria); and four different satellite-derived methodologies: DLR, University of Oldenburg Department of Energy and Semiconductor Research, GeoModel Solar, and HelioClim-3. Unlike the previous methods, the method of Mieslinger et al (2014) takes into consideration not only the systematic but also the random errors that are found when comparing satellite to ground data. Random errors are caused by parallax errors as well as by the difficulty in determining the vertical thickness of clouds from the satellite data used in the four methodologies applied here.…”

Section: Extrapolating Short-term Measured Data Setsmentioning

confidence: 99%

“…Despite the improvements offered by the method of Schumann et al (2009), significant biases in the DNI estimates remain, as demonstrated by Killius and Schroedter-Homscheidt (2012). A method to reduce the bias errors to near zero, and especially to minimize the biases of the highest radiation values that are used for design purposes, has been presented by Mieslinger et al (2014). The method was tested against two high-quality ground stations: Plataforma Solar de Almería (southern Spain) and Tamanrasset (Algeria); and four different satellite-derived methodologies: DLR, University of Oldenburg Department of Energy and Semiconductor Research, GeoModel Solar, and HelioClim-3.…”

Section: Extrapolating Short-term Measured Data Setsmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

152

121

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ForewordThe first version of this handbook was developed in response to a growing need by the solar energy industry for a single document addressing the key aspects of solar resource characterization. The solar energy industry has developed rapidly throughout the last few years, and there have been significant enhancements in the body of knowledge in the areas of solar resource assessment and forecasting. Thus, this second version of the handbook was developed from the need to update and enhance the initial version and present the state of the art in a condensed form for all of its users.Although the first version of this handbook was developed by only researchers from the National Renewable Energy Laboratory, this version has additional contributions from an international group of experts primarily from the knowledge that has been gained through participation in the International Energy Agency's Solar Heating and Cooling Programme Task 36 and Task 46.As in the first version, this material was assembled by scientists and engineers who have many decades of combined experience in atmospheric science, radiometry, meteorological data processing, and renewable energy technology development.Readers are encouraged to provide feedback to the authors for future revisions and an expansion of the handbook's scope and content. iv This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. PrefaceAs the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants. However, solar resource information is of particular interest for large installations, because they require a substantial investment, sometimes exceeding $1 billion in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need to have reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without this data, an accurate financial analysis is not possible.In September 2008, the U.S. Department of Energy (DOE) hosted a meeting of prominent CSP developers and stakeholders. One purpose was to identify areas in which the DOE's CSP program should focus its efforts to help the industry develop an...

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“…However, under clear sky conditions the inaccuracy in the determination of the aerosol loads (an even other atmospheric constituents) can have an important contribution to the model retrievals (Gueymard, 2011;Gueymard, 2012;Gueymard, 2014). The use of high quality ground data in hourly basis of the measured solar radiation components can allow for improvement of the satellite-based models, by reducing the bias or even systematic errors, that could result in more accurate mapping of the solar resource in a region (Cebecauer and Suri, 2012;Mieslinger et al, 2014;Polo et al, 2015).…”

Section: Solar Irradiation Raster Information Has Been Processed In Amentioning

confidence: 98%

Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites

Polo

2015

Journal of Atmospheric and Solar-Terrestrial Physics

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A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series

Cited by 25 publications

References 2 publications

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites

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